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The Netflix Prize was an open competition for the best collaborative filtering algorithm to predict user ratings for films, based on previous ratings without any other information about the users or films, i.e. without the users or the films being identified except by numbers assigned for the contest. The competition was held by Netflix, an online DVD-rental and online video streaming service, and was open to anyone not connected with Netflix (current and former employees, agents, close relatives of Netflix employees, etc.) or a resident of Cuba, Iran, Syria, North Korea, Myanmar or Sudan.〔(【引用サイトリンク】 The Netflix Prize Rules )〕 On 21 September 2009, the grand prize of was given to the BellKor's Pragmatic Chaos team which bested Netflix's own algorithm for predicting ratings by 10.06%.〔(【引用サイトリンク】 The Netflix Prize )〕 == Problem and data sets == Netflix provided a ''training'' data set of 100,480,507 ratings that 480,189 users gave to 17,770 movies. Each training rating is a quadruplet of the form The ''qualifying'' data set contains over 2,817,131 triplets of the form In summary, the data used in the Netflix Prize looks as follows: * Training set (99,072,112 ratings not including the probe set, 100,480,507 including the probe set) * * Probe set (1,408,395 ratings) * Qualifying set (2,817,131 ratings) consisting of: * * Test set (1,408,789 ratings), used to determine winners * * Quiz set (1,408,342 ratings), used to calculate leaderboard scores For each movie, title and year of release are provided in a separate dataset. No information at all is provided about users. In order to protect the privacy of customers, "some of the rating data for some customers in the training and qualifying sets have been deliberately perturbed in one or more of the following ways: deleting ratings; inserting alternative ratings and dates; and modifying rating dates".〔 The training set is such that the average user rated over 200 movies, and the average movie was rated by over 5000 users. But there is wide variance in the data—some movies in the training set have as few as 3 ratings, while one user rated over 17,000 movies. There was some controversy as to the choice of RMSE as the defining metric. Would a reduction of the RMSE by 10% really benefit the users? It has been claimed that even as small an improvement as 1% RMSE results in a significant difference in the ranking of the "top-10" most recommended movies for a user. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Netflix Prize」の詳細全文を読む スポンサード リンク
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